TESS Background data¶

This notebook shows how to use BackgroundCube to get TESS Full Frame Images (FFI) scatter light component and Earth/Moon angles as pixel maps for every camera/ccd in the instrument.

This data could be used to train a machine learning model or used directly to correct the scatter light contribution in TESS cutouts.

In [1]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

In [2]:

# basic imports
import os
import numpy as np
from tess_backml import BackgroundCube
from tess_backml.utils import pooling_2d
import matplotlib.pyplot as plt

# increase animation frame limits
import matplotlib
matplotlib.rcParams["animation.embed_limit"] = 2**128

# basic imports import os import numpy as np from tess_backml import BackgroundCube from tess_backml.utils import pooling_2d import matplotlib.pyplot as plt # increase animation frame limits import matplotlib matplotlib.rcParams["animation.embed_limit"] = 2**128

In [3]:

# define sector/camera/ccd
sector = 2
camera = 1
ccd = 1
binsize = 16

# define sector/camera/ccd sector = 2 camera = 1 ccd = 1 binsize = 16

We initialize the object with a specific Sectopr/Camera/CCD, an image bin size, and a method for downsizing (default if 'binning')

In [4]:

bkg_data = BackgroundCube(
    sector=sector, camera=camera, ccd=ccd, img_bin=binsize, downsize="binning"
)
print(bkg_data)

bkg_data = BackgroundCube( sector=sector, camera=camera, ccd=ccd, img_bin=binsize, downsize="binning" ) print(bkg_data)

TESS FFI Background object (Sector, Camera, CCD, N times): 2, 1, 1, 1245

Compute the binned scatter light cube which does:

• Finds the darkest frame in the sector to build a star mask and a saturated pixel mask
• Adds a strap mask
• Pulls the FFI flux data from the cloud
• Downsize the image by doing median 16x16 binning, masking out stars, straps, and saturated pixels
• The resulting scatter light cube has shape [nt, 128, 128]

In [ ]:

bkg_data.get_scatter_light_cube(frames=None, mask_straps=True, plot=True)

bkg_data.get_scatter_light_cube(frames=None, mask_straps=True, plot=True)

Computing sector darkest frames...

INFO:tess_backml:Computing sector darkest frames...

Computing star mask...

INFO:tess_backml:Computing star mask...

Getting FFI flux cube...

INFO:tess_backml:Getting FFI flux cube...

Computing average static scene from darkes frames...

INFO:tess_backml:Computing average static scene from darkes frames...

Iterating frames:  58%|█████▊    | 717/1245 [16:11<11:24,  1.30s/it]

We can animate the scatter light cube for visualization

In [6]:

bkg_data.animate_data(data="sl", save=False, step=10);

bkg_data.animate_data(data="sl", save=False, step=10);

INFO:matplotlib.animation:Animation.save using <class 'matplotlib.animation.HTMLWriter'>

Out[6]:

Once Loop Reflect

Now we compute the Earth/Moon vector maps:

• We pull the Earth/Moon distance, azimuth, and elevation angles from the engineering files. This values a w.r.t. each camera's boresight and changes for each frame in the sector. Using trigonometric rules, we compute these values for each pixel in the downsized grid [nt, 128, 128].
• If ang_size == True then the Earth/Moon distances are changed to angular size.

In [ ]:

bkg_data.get_vector_maps(ang_size=True)

bkg_data.get_vector_maps(ang_size=True)

We can inspect these maps for a single frame

In [ ]:

tdx = 500

fig, ax = plt.subplots(2, 3, figsize=(9,6), sharex=True, sharey=True)

fig.suptitle(
    f"Sector {sector} Camera {camera} CCD {ccd} | "
    f"Frame {tdx} Time {bkg_data.time[tdx]:.3f}"
)
ax[0, 0].imshow(bkg_data.earth_maps["alt"][tdx], origin="lower")
ax[0, 1].imshow(bkg_data.earth_maps["az"][tdx], origin="lower")
ax[0, 2].imshow(bkg_data.earth_maps["dist"][tdx], origin="lower")

ax[0, 0].set_title("Earth Altitude Angle")
ax[0, 1].set_title("Earth Azimuth Angle")
ax[0, 2].set_title("Earth Angular Size")

ax[1, 0].imshow(bkg_data.moon_maps["alt"][tdx], origin="lower")
ax[1, 1].imshow(bkg_data.moon_maps["az"][tdx], origin="lower")
ax[1, 2].imshow(bkg_data.moon_maps["dist"][tdx], origin="lower")

ax[1, 0].set_title("Moon Altitude Angle")
ax[1, 1].set_title("Moon Azimuth Angle")
ax[1, 2].set_title("Moon Angular Size")

ax[0, 0].set_ylabel("Pixel Row")
ax[1, 0].set_ylabel("Pixel Row")
ax[1, 0].set_xlabel("Pixel Column")
ax[1, 1].set_xlabel("Pixel Column")
ax[1, 2].set_xlabel("Pixel Column")

plt.show()

tdx = 500 fig, ax = plt.subplots(2, 3, figsize=(9,6), sharex=True, sharey=True) fig.suptitle( f"Sector {sector} Camera {camera} CCD {ccd} | " f"Frame {tdx} Time {bkg_data.time[tdx]:.3f}" ) ax[0, 0].imshow(bkg_data.earth_maps["alt"][tdx], origin="lower") ax[0, 1].imshow(bkg_data.earth_maps["az"][tdx], origin="lower") ax[0, 2].imshow(bkg_data.earth_maps["dist"][tdx], origin="lower") ax[0, 0].set_title("Earth Altitude Angle") ax[0, 1].set_title("Earth Azimuth Angle") ax[0, 2].set_title("Earth Angular Size") ax[1, 0].imshow(bkg_data.moon_maps["alt"][tdx], origin="lower") ax[1, 1].imshow(bkg_data.moon_maps["az"][tdx], origin="lower") ax[1, 2].imshow(bkg_data.moon_maps["dist"][tdx], origin="lower") ax[1, 0].set_title("Moon Altitude Angle") ax[1, 1].set_title("Moon Azimuth Angle") ax[1, 2].set_title("Moon Angular Size") ax[0, 0].set_ylabel("Pixel Row") ax[1, 0].set_ylabel("Pixel Row") ax[1, 0].set_xlabel("Pixel Column") ax[1, 1].set_xlabel("Pixel Column") ax[1, 2].set_xlabel("Pixel Column") plt.show()

In [11]:

bkg_data.nt, bkg_data.quality.sum()

bkg_data.nt, bkg_data.quality.sum()

Out[11]:

(1245, 10)

In [10]:

bkg_data.scatter_cube.shape, bkg_data.earth_vectors["dist"].shape

bkg_data.scatter_cube.shape, bkg_data.earth_vectors["dist"].shape

Out[10]:

((1235, 128, 128), (1245,))

The computed scatter light, angle maps, and times values can be saved to disk as a npz file for future use.

In [16]:

# save data to disk
fname = f"./data/ffi_cubes_bin{bkg_data.img_bin}_sector{sector:03}_{camera}-{ccd}_light.npz"
bkg_data.save_data(out_file=fname, save_maps=True)

# save data to disk fname = f"./data/ffi_cubes_bin{bkg_data.img_bin}_sector{sector:03}_{camera}-{ccd}_light.npz" bkg_data.save_data(out_file=fname, save_maps=True)

In [17]:

data = np.load(fname, )
data.items()

data = np.load(fname, ) data.items()

Out[17]:

ItemsView(NpzFile './data/ffi_cubes_bin16_sector002_1-1_light.npz' with keys: scatter_cube, time, cadenceno, earth_alt_map, earth_az_map...)

In [15]:

data["scatter_cube"].shape, data["earth_alt_map"].shape

data["scatter_cube"].shape, data["earth_alt_map"].shape

Out[15]:

((1235, 128, 128), (1235, 128, 128))

In [21]:

bar = plt.imshow(data["scatter_cube"][1180], origin="lower", vmin=0, vmax=120,)
plt.colorbar(bar)
plt.show()

bar = plt.imshow(data["scatter_cube"][1180], origin="lower", vmin=0, vmax=120,) plt.colorbar(bar) plt.show()

In [ ]: